[10]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Step 0: Load the data
df = pd.read_csv(r"C:\Users\pappu\Downloads\DMP-01-Inclass-Exercises-File\DMP-01 Inclass Exercises File\Batch 66\DMP_01_data_TATAMOTORS.NS.csv", index_col=0, parse_dates=True)
df3 = df.copy()
# Step 1: Compute SMA50 and SMA200
df3['sma50'] = df3['Adj Close'].rolling(window=50).mean().shift(1)
df3['sma200'] = df3['Adj Close'].rolling(window=200).mean().shift(1)
# Step 2: Previous day's SMAs
df3['sma50_prev_day'] = df3['sma50'].shift(1)
df3['sma200_prev_day'] = df3['sma200'].shift(1)
# Step 3: Generate crossover signals
df3['signal'] = np.where(
(df3['sma50'] > df3['sma200']) & (df3['sma50_prev_day'] < df3['sma200_prev_day']), 1, 0
)
df3['signal'] = np.where(
(df3['sma50'] < df3['sma200']) & (df3['sma50_prev_day'] > df3['sma200_prev_day']), -1, df3['signal']
)
# Step 4: Buy and Sell prices
df3['buy_price'] = df3.apply(lambda x: x['Adj Close'] if x['signal'] == 1 else 0, axis=1)
df3['sell_price'] = df3.apply(lambda x: -x['Adj Close'] if x['signal'] == -1 else 0, axis=1)
# Step 5: Create trade_price and position
df3['trade_price'] = df3['buy_price'] + df3['sell_price']
df3['trade_price'] = df3['trade_price'].replace(0, np.nan).ffill()
df3['position'] = df3['signal'].replace(0, np.nan).ffill()
df3['position'] = df3['position'].fillna(0)
# Step 6: Strategy returns
df3['cc_returns'] = df3['Adj Close'].pct_change()
df3['strategy_returns'] = df3['cc_returns'] * df3['position']
# Step 7: Cumulative returns
df3[['cc_returns', 'strategy_returns']] = df3[['cc_returns', 'strategy_returns']].fillna(0)
df3[['cc_returns', 'strategy_returns']] = 1 + df3[['cc_returns', 'strategy_returns']]
df3[['cc_returns', 'strategy_returns']] = df3[['cc_returns', 'strategy_returns']].cumprod()
# Step 8: Drawdown calculation
df3['strategy_cummax'] = df3['strategy_returns'].cummax()
df3['drawdown'] = df3['strategy_returns'] / df3['strategy_cummax'] - 1
# Step 9: Plot cumulative returns and drawdown
plt.figure(figsize=(14, 6))
# Plot Cumulative Returns
plt.subplot(2, 1, 1)
plt.plot(df3['cc_returns'], label='Buy & Hold', linewidth=1.5)
plt.plot(df3['strategy_returns'], label='Strategy', linewidth=1.5)
plt.title('Cumulative Returns')
plt.legend()
plt.grid(True)
# Plot Drawdown
plt.subplot(2, 1, 2)
plt.plot(df3['drawdown'], color='red', label='Drawdown')
plt.title('Strategy Drawdown')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
# Step 10: Print final results
print("📈 Buy & Hold Return:", round(df3['cc_returns'].iloc[-1], 2))
print("📊 Strategy Return:", round(df3['strategy_returns'].iloc[-1], 2))
print("📉 Max Drawdown:", round(df3['drawdown'].min() * 100, 2), "%")
📈 Buy & Hold Return: 1.53 📊 Strategy Return: 1.28 📉 Max Drawdown: -93.38 %
[4]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Load data
df = pd.read_csv(r"C:\Users\pappu\Downloads\DMP-01-Inclass-Exercises-File\DMP-01 Inclass Exercises File\Batch 66\DMP_01_data_TATAMOTORS.NS.csv", index_col=0, parse_dates=True)
df = df[['Adj Close']].copy()
# EMA calculations
df['EMA_20'] = df['Adj Close'].ewm(span=20, adjust=False).mean().shift(1)
df['EMA_50'] = df['Adj Close'].ewm(span=50, adjust=False).mean().shift(1)
df['EMA_20_prev'] = df['EMA_20'].shift(1)
df['EMA_50_prev'] = df['EMA_50'].shift(1)
# Signal generation
df['signal'] = np.where((df['EMA_20'] > df['EMA_50']) & (df['EMA_20_prev'] <= df['EMA_50_prev']), 1, 0)
df['signal'] = np.where((df['EMA_20'] < df['EMA_50']) & (df['EMA_20_prev'] >= df['EMA_50_prev']), -1, df['signal'])
# Trade prices
df['buy_price'] = df.apply(lambda x: x['Adj Close'] if x['signal'] == 1 else 0, axis=1)
df['sell_price'] = df.apply(lambda x: -x['Adj Close'] if x['signal'] == -1 else 0, axis=1)
df['trade_price'] = df['buy_price'] + df['sell_price']
df['trade_price'] = df['trade_price'].mask(df['trade_price'] == 0).ffill()
# Position tracking
df['position'] = df['signal'].mask(df['signal'] == 0).ffill()
# Strategy performance
df['cc_returns'] = df['Adj Close'].pct_change()
df['strategy_returns'] = df['cc_returns'] * df['position']
df[['cc_returns', 'strategy_returns']] = df[['cc_returns', 'strategy_returns']].fillna(0)
df[['cc_returns', 'strategy_returns']] = 1 + df[['cc_returns', 'strategy_returns']]
df[['cc_returns', 'strategy_returns']] = df[['cc_returns', 'strategy_returns']].cumprod()
# Drawdown calculation
df['strategy_cummax'] = df['strategy_returns'].cummax()
df['drawdown'] = df['strategy_returns'] / df['strategy_cummax'] - 1
# Plot
plt.figure(figsize=(14, 6))
plt.subplot(2, 1, 1)
plt.plot(df['cc_returns'], label='Buy & Hold', linewidth=1.5)
plt.plot(df['strategy_returns'], label='EMA 20/50 Strategy', linewidth=1.5)
plt.legend()
plt.title('Cumulative Returns')
plt.subplot(2, 1, 2)
plt.plot(df['drawdown'], color='red', label='Drawdown')
plt.title('Strategy Drawdown')
plt.grid(True)
plt.tight_layout()
plt.show()
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